Overview

Dataset statistics

Number of variables74
Number of observations145460
Missing cells162705
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.2 MiB
Average record size in memory232.0 B

Variable types

Categorical51
Numeric23

Alerts

MinTemp is highly overall correlated with MaxTemp and 2 other fieldsHigh correlation
MaxTemp is highly overall correlated with MinTemp and 4 other fieldsHigh correlation
Evaporation is highly overall correlated with MaxTemp and 2 other fieldsHigh correlation
Sunshine is highly overall correlated with Cloud3pmHigh correlation
WindGustSpeed is highly overall correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly overall correlated with WindGustSpeedHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeedHigh correlation
Humidity9am is highly overall correlated with Humidity3pmHigh correlation
Humidity3pm is highly overall correlated with Humidity9am and 3 other fieldsHigh correlation
Pressure9am is highly overall correlated with Pressure3pm and 4 other fieldsHigh correlation
Pressure3pm is highly overall correlated with Pressure9am and 4 other fieldsHigh correlation
Cloud9am is highly overall correlated with Humidity3pm and 12 other fieldsHigh correlation
Cloud3pm is highly overall correlated with Sunshine and 13 other fieldsHigh correlation
Temp9am is highly overall correlated with MinTemp and 3 other fieldsHigh correlation
Temp3pm is highly overall correlated with MinTemp and 5 other fieldsHigh correlation
WindGustDir1 is highly overall correlated with WindDir3pm1High correlation
WindDir3pm1 is highly overall correlated with WindGustDir1High correlation
Location is highly overall correlated with Albany and 47 other fieldsHigh correlation
Albany is highly overall correlated with LocationHigh correlation
Albury is highly overall correlated with LocationHigh correlation
AliceSprings is highly overall correlated with LocationHigh correlation
BadgerysCreek is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Ballarat is highly overall correlated with LocationHigh correlation
Bendigo is highly overall correlated with LocationHigh correlation
Brisbane is highly overall correlated with LocationHigh correlation
Cairns is highly overall correlated with LocationHigh correlation
Canberra is highly overall correlated with LocationHigh correlation
Cobar is highly overall correlated with LocationHigh correlation
CoffsHarbour is highly overall correlated with LocationHigh correlation
Dartmoor is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Darwin is highly overall correlated with LocationHigh correlation
GoldCoast is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Hobart is highly overall correlated with LocationHigh correlation
Katherine is highly overall correlated with LocationHigh correlation
Launceston is highly overall correlated with LocationHigh correlation
Melbourne is highly overall correlated with LocationHigh correlation
MelbourneAirport is highly overall correlated with LocationHigh correlation
Mildura is highly overall correlated with LocationHigh correlation
Moree is highly overall correlated with LocationHigh correlation
MountGambier is highly overall correlated with LocationHigh correlation
MountGinini is highly overall correlated with MaxTemp and 6 other fieldsHigh correlation
Newcastle is highly overall correlated with Pressure9am and 2 other fieldsHigh correlation
Nhil is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
NorahHead is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
NorfolkIsland is highly overall correlated with LocationHigh correlation
Nuriootpa is highly overall correlated with LocationHigh correlation
PearceRAAF is highly overall correlated with LocationHigh correlation
Penrith is highly overall correlated with Pressure9am and 4 other fieldsHigh correlation
Perth is highly overall correlated with LocationHigh correlation
PerthAirport is highly overall correlated with LocationHigh correlation
Portland is highly overall correlated with LocationHigh correlation
Richmond is highly overall correlated with LocationHigh correlation
Sale is highly overall correlated with LocationHigh correlation
SalmonGums is highly overall correlated with Pressure9am and 4 other fieldsHigh correlation
Sydney is highly overall correlated with LocationHigh correlation
SydneyAirport is highly overall correlated with LocationHigh correlation
Townsville is highly overall correlated with LocationHigh correlation
Tuggeranong is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Uluru is highly overall correlated with LocationHigh correlation
WaggaWagga is highly overall correlated with LocationHigh correlation
Walpole is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Watsonia is highly overall correlated with LocationHigh correlation
Williamtown is highly overall correlated with LocationHigh correlation
Witchcliffe is highly overall correlated with Cloud9am and 2 other fieldsHigh correlation
Wollongong is highly overall correlated with LocationHigh correlation
Woomera is highly overall correlated with LocationHigh correlation
Albany is highly imbalanced (85.4%)Imbalance
Albury is highly imbalanced (85.4%)Imbalance
AliceSprings is highly imbalanced (85.4%)Imbalance
BadgerysCreek is highly imbalanced (85.5%)Imbalance
Ballarat is highly imbalanced (85.4%)Imbalance
Bendigo is highly imbalanced (85.4%)Imbalance
Brisbane is highly imbalanced (84.8%)Imbalance
Cairns is highly imbalanced (85.4%)Imbalance
Canberra is highly imbalanced (83.9%)Imbalance
Cobar is highly imbalanced (85.5%)Imbalance
CoffsHarbour is highly imbalanced (85.5%)Imbalance
Dartmoor is highly imbalanced (85.5%)Imbalance
Darwin is highly imbalanced (84.8%)Imbalance
GoldCoast is highly imbalanced (85.4%)Imbalance
Hobart is highly imbalanced (84.8%)Imbalance
Katherine is highly imbalanced (91.4%)Imbalance
Launceston is highly imbalanced (85.4%)Imbalance
Melbourne is highly imbalanced (84.8%)Imbalance
MelbourneAirport is highly imbalanced (85.5%)Imbalance
Mildura is highly imbalanced (85.5%)Imbalance
Moree is highly imbalanced (85.5%)Imbalance
MountGambier is highly imbalanced (85.4%)Imbalance
MountGinini is highly imbalanced (85.4%)Imbalance
Newcastle is highly imbalanced (85.4%)Imbalance
Nhil is highly imbalanced (91.4%)Imbalance
NorahHead is highly imbalanced (85.5%)Imbalance
NorfolkIsland is highly imbalanced (85.5%)Imbalance
Nuriootpa is highly imbalanced (85.5%)Imbalance
PearceRAAF is highly imbalanced (85.5%)Imbalance
Penrith is highly imbalanced (85.4%)Imbalance
Perth is highly imbalanced (84.8%)Imbalance
PerthAirport is highly imbalanced (85.5%)Imbalance
Portland is highly imbalanced (85.5%)Imbalance
Richmond is highly imbalanced (85.5%)Imbalance
Sale is highly imbalanced (85.5%)Imbalance
SalmonGums is highly imbalanced (85.5%)Imbalance
Sydney is highly imbalanced (84.2%)Imbalance
SydneyAirport is highly imbalanced (85.5%)Imbalance
Townsville is highly imbalanced (85.4%)Imbalance
Tuggeranong is highly imbalanced (85.4%)Imbalance
Uluru is highly imbalanced (91.4%)Imbalance
WaggaWagga is highly imbalanced (85.5%)Imbalance
Walpole is highly imbalanced (85.5%)Imbalance
Watsonia is highly imbalanced (85.5%)Imbalance
Williamtown is highly imbalanced (85.5%)Imbalance
Witchcliffe is highly imbalanced (85.5%)Imbalance
Wollongong is highly imbalanced (85.4%)Imbalance
Woomera is highly imbalanced (85.5%)Imbalance
WindSpeed9am has 1767 (1.2%) missing valuesMissing
WindSpeed3pm has 3062 (2.1%) missing valuesMissing
Humidity9am has 2654 (1.8%) missing valuesMissing
Humidity3pm has 4507 (3.1%) missing valuesMissing
Pressure9am has 15065 (10.4%) missing valuesMissing
Pressure3pm has 15028 (10.3%) missing valuesMissing
Cloud9am has 55888 (38.4%) missing valuesMissing
Cloud3pm has 59358 (40.8%) missing valuesMissing
Temp9am has 1767 (1.2%) missing valuesMissing
Temp3pm has 3609 (2.5%) missing valuesMissing
Rainfall has 94341 (64.9%) zerosZeros
Sunshine has 72194 (49.6%) zerosZeros
WindSpeed9am has 8745 (6.0%) zerosZeros
Cloud9am has 8642 (5.9%) zerosZeros
Cloud3pm has 4974 (3.4%) zerosZeros
WindGustDir1 has 9181 (6.3%) zerosZeros
WindDir9am1 has 9176 (6.3%) zerosZeros
WindDir3pm1 has 8472 (5.8%) zerosZeros
week has 20745 (14.3%) zerosZeros

Reproduction

Analysis started2022-12-28 11:25:35.674403
Analysis finished2022-12-28 11:27:46.999141
Duration2 minutes and 11.32 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

Location
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3436
Sydney
 
3344
Darwin
 
3193
Melbourne
 
3193
Brisbane
 
3193
Other values (44)
129101 

Length

Max length16
Median length11
Mean length8.7116252
Min length4

Characters and Unicode

Total characters1267193
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Canberra 3436
 
2.4%
Sydney 3344
 
2.3%
Darwin 3193
 
2.2%
Melbourne 3193
 
2.2%
Brisbane 3193
 
2.2%
Adelaide 3193
 
2.2%
Perth 3193
 
2.2%
Hobart 3193
 
2.2%
Albany 3040
 
2.1%
MountGambier 3040
 
2.1%
Other values (39) 113442
78.0%

Length

2022-12-28T16:57:47.077707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra 3436
 
2.4%
sydney 3344
 
2.3%
darwin 3193
 
2.2%
melbourne 3193
 
2.2%
brisbane 3193
 
2.2%
adelaide 3193
 
2.2%
perth 3193
 
2.2%
hobart 3193
 
2.2%
launceston 3040
 
2.1%
wollongong 3040
 
2.1%
Other values (39) 113442
78.0%

Most occurring characters

ValueCountFrequency (%)
a 117797
 
9.3%
r 116473
 
9.2%
o 109016
 
8.6%
e 104586
 
8.3%
n 90638
 
7.2%
l 79075
 
6.2%
i 76233
 
6.0%
t 59332
 
4.7%
d 36868
 
2.9%
u 36585
 
2.9%
Other values (30) 440590
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1070469
84.5%
Uppercase Letter 196724
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 117797
11.0%
r 116473
10.9%
o 109016
10.2%
e 104586
9.8%
n 90638
 
8.5%
l 79075
 
7.4%
i 76233
 
7.1%
t 59332
 
5.5%
d 36868
 
3.4%
u 36585
 
3.4%
Other values (12) 243866
22.8%
Uppercase Letter
ValueCountFrequency (%)
A 27358
13.9%
W 24100
12.3%
C 18543
9.4%
M 18300
9.3%
S 15403
7.8%
P 15259
7.8%
N 13639
6.9%
B 12282
6.2%
G 12121
 
6.2%
H 9206
 
4.7%
Other values (8) 30513
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1267193
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 117797
 
9.3%
r 116473
 
9.2%
o 109016
 
8.6%
e 104586
 
8.3%
n 90638
 
7.2%
l 79075
 
6.2%
i 76233
 
6.0%
t 59332
 
4.7%
d 36868
 
2.9%
u 36585
 
2.9%
Other values (30) 440590
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1267193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 117797
 
9.3%
r 116473
 
9.2%
o 109016
 
8.6%
e 104586
 
8.3%
n 90638
 
7.2%
l 79075
 
6.2%
i 76233
 
6.0%
t 59332
 
4.7%
d 36868
 
2.9%
u 36585
 
2.9%
Other values (30) 440590
34.8%

MinTemp
Real number (ℝ)

Distinct389
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.181844
Minimum-8.5
Maximum33.9
Zeros159
Zeros (%)0.1%
Negative3464
Negative (%)2.4%
Memory size1.1 MiB
2022-12-28T16:57:47.218809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.7
median11.9
Q316.8
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.3668813
Coefficient of variation (CV)0.52265331
Kurtosis-0.45963009
Mean12.181844
Median Absolute Deviation (MAD)4.5
Skewness0.0269624
Sum1771971.1
Variance40.537178
MonotonicityNot monotonic
2022-12-28T16:57:47.344222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 2384
 
1.6%
10.2 898
 
0.6%
9.6 896
 
0.6%
10.5 884
 
0.6%
9 872
 
0.6%
10.8 872
 
0.6%
10 871
 
0.6%
12 866
 
0.6%
8.9 861
 
0.6%
10.4 860
 
0.6%
Other values (379) 135196
92.9%
ValueCountFrequency (%)
-8.5 1
 
< 0.1%
-8.2 2
 
< 0.1%
-8 2
 
< 0.1%
-7.8 1
 
< 0.1%
-7.6 2
 
< 0.1%
-7.5 2
 
< 0.1%
-7.3 1
 
< 0.1%
-7.2 1
 
< 0.1%
-7.1 1
 
< 0.1%
-7 7
< 0.1%
ValueCountFrequency (%)
33.9 1
 
< 0.1%
31.9 1
 
< 0.1%
31.8 1
 
< 0.1%
31.4 3
< 0.1%
31.2 1
 
< 0.1%
31 1
 
< 0.1%
30.7 2
< 0.1%
30.5 1
 
< 0.1%
30.3 1
 
< 0.1%
30.2 1
 
< 0.1%

MaxTemp
Real number (ℝ)

Distinct505
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.193422
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Negative113
Negative (%)0.1%
Memory size1.1 MiB
2022-12-28T16:57:47.470111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.9
Q118
median22.5
Q328.2
95-th percentile35.4
Maximum48.1
Range52.9
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation7.0944118
Coefficient of variation (CV)0.30588034
Kurtosis-0.20634012
Mean23.193422
Median Absolute Deviation (MAD)5
Skewness0.23221122
Sum3373715.2
Variance50.330679
MonotonicityNot monotonic
2022-12-28T16:57:47.626765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 2146
 
1.5%
19 843
 
0.6%
19.8 840
 
0.6%
20.4 834
 
0.6%
19.9 823
 
0.6%
20.8 817
 
0.6%
19.5 812
 
0.6%
18.5 811
 
0.6%
21 810
 
0.6%
18.2 804
 
0.6%
Other values (495) 135920
93.4%
ValueCountFrequency (%)
-4.8 1
< 0.1%
-4.1 1
< 0.1%
-3.8 1
< 0.1%
-3.7 1
< 0.1%
-3.2 1
< 0.1%
-3.1 2
< 0.1%
-3 1
< 0.1%
-2.9 1
< 0.1%
-2.7 1
< 0.1%
-2.5 2
< 0.1%
ValueCountFrequency (%)
48.1 1
 
< 0.1%
47.3 2
< 0.1%
47 1
 
< 0.1%
46.9 1
 
< 0.1%
46.8 3
< 0.1%
46.7 2
< 0.1%
46.6 1
 
< 0.1%
46.5 1
 
< 0.1%
46.4 4
< 0.1%
46.3 2
< 0.1%

Rainfall
Real number (ℝ)

Distinct681
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3079898
Minimum0
Maximum371
Zeros94341
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:47.769777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.6
95-th percentile12.8
Maximum371
Range371
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation8.3897709
Coefficient of variation (CV)3.6350988
Kurtosis181.91436
Mean2.3079898
Median Absolute Deviation (MAD)0
Skewness9.940909
Sum335720.2
Variance70.388257
MonotonicityNot monotonic
2022-12-28T16:57:47.909278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94341
64.9%
0.2 8761
 
6.0%
0.4 3782
 
2.6%
0.6 2592
 
1.8%
0.8 2056
 
1.4%
1 1759
 
1.2%
1.2 1535
 
1.1%
1.4 1377
 
0.9%
1.6 1200
 
0.8%
1.8 1104
 
0.8%
Other values (671) 26953
 
18.5%
ValueCountFrequency (%)
0 94341
64.9%
0.1 157
 
0.1%
0.2 8761
 
6.0%
0.3 65
 
< 0.1%
0.4 3782
 
2.6%
0.5 39
 
< 0.1%
0.6 2592
 
1.8%
0.7 13
 
< 0.1%
0.8 2056
 
1.4%
0.9 15
 
< 0.1%
ValueCountFrequency (%)
371 1
< 0.1%
367.6 1
< 0.1%
278.4 1
< 0.1%
268.6 1
< 0.1%
247.2 1
< 0.1%
240 1
< 0.1%
236.8 1
< 0.1%
225 1
< 0.1%
219.6 1
< 0.1%
216.3 1
< 0.1%

Evaporation
Real number (ℝ)

Distinct358
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8344473
Minimum0
Maximum145
Zeros244
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:48.070003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q14
median4
Q35.2
95-th percentile10.6
Maximum145
Range145
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation3.2441031
Coefficient of variation (CV)0.6710391
Kurtosis77.07802
Mean4.8344473
Median Absolute Deviation (MAD)0.4
Skewness5.1714289
Sum703218.7
Variance10.524205
MonotonicityNot monotonic
2022-12-28T16:57:48.207470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 66129
45.5%
8 2609
 
1.8%
2.2 2095
 
1.4%
2 2032
 
1.4%
2.4 2003
 
1.4%
2.6 2003
 
1.4%
1.8 1979
 
1.4%
3 1973
 
1.4%
3.4 1967
 
1.4%
3.2 1956
 
1.3%
Other values (348) 60714
41.7%
ValueCountFrequency (%)
0 244
 
0.2%
0.1 8
 
< 0.1%
0.2 503
 
0.3%
0.3 10
 
< 0.1%
0.4 769
0.5%
0.5 14
 
< 0.1%
0.6 1097
0.8%
0.7 24
 
< 0.1%
0.8 1384
1.0%
0.9 28
 
< 0.1%
ValueCountFrequency (%)
145 1
< 0.1%
86.2 1
< 0.1%
82.4 1
< 0.1%
81.2 1
< 0.1%
77.3 1
< 0.1%
74.8 1
< 0.1%
72.2 1
< 0.1%
70.4 1
< 0.1%
70 1
< 0.1%
68.8 2
< 0.1%

Sunshine
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct145
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9570693
Minimum0
Maximum14.5
Zeros72194
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:48.364077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q38.7
95-th percentile12.1
Maximum14.5
Range14.5
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation4.6807853
Coefficient of variation (CV)1.1828919
Kurtosis-1.2619176
Mean3.9570693
Median Absolute Deviation (MAD)0.1
Skewness0.61712375
Sum575595.3
Variance21.909751
MonotonicityNot monotonic
2022-12-28T16:57:48.505583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72194
49.6%
10.7 1101
 
0.8%
11 1094
 
0.8%
10.8 1069
 
0.7%
10.5 1027
 
0.7%
10.9 1021
 
0.7%
10.3 1010
 
0.7%
10.2 993
 
0.7%
10 984
 
0.7%
11.1 978
 
0.7%
Other values (135) 63989
44.0%
ValueCountFrequency (%)
0 72194
49.6%
0.1 542
 
0.4%
0.2 521
 
0.4%
0.3 433
 
0.3%
0.4 326
 
0.2%
0.5 322
 
0.2%
0.6 298
 
0.2%
0.7 344
 
0.2%
0.8 320
 
0.2%
0.9 323
 
0.2%
ValueCountFrequency (%)
14.5 1
 
< 0.1%
14.3 4
 
< 0.1%
14.2 2
 
< 0.1%
14.1 6
 
< 0.1%
14 15
 
< 0.1%
13.9 22
 
< 0.1%
13.8 60
 
< 0.1%
13.7 118
0.1%
13.6 181
0.1%
13.5 183
0.1%

WindGustSpeed
Real number (ℝ)

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.679967
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:48.670867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median37
Q346
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.181471
Coefficient of variation (CV)0.33219461
Kurtosis1.7653882
Mean39.679967
Median Absolute Deviation (MAD)7
Skewness0.97142612
Sum5771848
Variance173.75119
MonotonicityNot monotonic
2022-12-28T16:57:48.788249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 19478
 
13.4%
39 8794
 
6.0%
31 8428
 
5.8%
37 8047
 
5.5%
33 7933
 
5.5%
41 7369
 
5.1%
30 7038
 
4.8%
43 6609
 
4.5%
28 6478
 
4.5%
44 5432
 
3.7%
Other values (57) 59854
41.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 19
 
< 0.1%
9 91
 
0.1%
11 192
 
0.1%
13 532
 
0.4%
15 835
 
0.6%
17 1387
1.0%
19 1751
1.2%
20 2627
1.8%
22 2810
1.9%
ValueCountFrequency (%)
135 3
 
< 0.1%
130 1
 
< 0.1%
126 2
 
< 0.1%
124 2
 
< 0.1%
122 3
 
< 0.1%
120 4
< 0.1%
117 4
< 0.1%
115 5
< 0.1%
113 8
< 0.1%
111 3
 
< 0.1%

WindSpeed9am
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct43
Distinct (%)< 0.1%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean14.043426
Minimum0
Maximum130
Zeros8745
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:48.898047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9153753
Coefficient of variation (CV)0.63484333
Kurtosis1.2269909
Mean14.043426
Median Absolute Deviation (MAD)6
Skewness0.77762951
Sum2017942
Variance79.483917
MonotonicityNot monotonic
2022-12-28T16:57:49.007903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9 13649
 
9.4%
13 13132
 
9.0%
11 11728
 
8.1%
17 10788
 
7.4%
7 10783
 
7.4%
15 10625
 
7.3%
6 9118
 
6.3%
19 8763
 
6.0%
0 8745
 
6.0%
20 8063
 
5.5%
Other values (33) 38299
26.3%
ValueCountFrequency (%)
0 8745
6.0%
2 4609
 
3.2%
4 6360
4.4%
6 9118
6.3%
7 10783
7.4%
9 13649
9.4%
11 11728
8.1%
13 13132
9.0%
15 10625
7.3%
17 10788
7.4%
ValueCountFrequency (%)
130 1
 
< 0.1%
87 2
 
< 0.1%
83 1
 
< 0.1%
74 4
 
< 0.1%
72 1
 
< 0.1%
69 2
 
< 0.1%
67 4
 
< 0.1%
65 8
< 0.1%
63 9
< 0.1%
61 11
< 0.1%

WindSpeed3pm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)< 0.1%
Missing3062
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean18.662657
Minimum0
Maximum87
Zeros1112
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:49.117725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8098
Coefficient of variation (CV)0.47205498
Kurtosis0.76385824
Mean18.662657
Median Absolute Deviation (MAD)6
Skewness0.62821542
Sum2657525
Variance77.612576
MonotonicityNot monotonic
2022-12-28T16:57:49.243209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
13 12580
 
8.6%
17 12539
 
8.6%
20 11713
 
8.1%
15 11483
 
7.9%
19 11263
 
7.7%
11 10015
 
6.9%
9 9753
 
6.7%
24 9052
 
6.2%
22 8598
 
5.9%
28 6553
 
4.5%
Other values (34) 38849
26.7%
ValueCountFrequency (%)
0 1112
 
0.8%
2 1034
 
0.7%
4 2249
 
1.5%
6 3805
 
2.6%
7 5903
4.1%
9 9753
6.7%
11 10015
6.9%
13 12580
8.6%
15 11483
7.9%
17 12539
8.6%
ValueCountFrequency (%)
87 1
 
< 0.1%
83 2
 
< 0.1%
78 1
 
< 0.1%
76 2
 
< 0.1%
74 1
 
< 0.1%
72 2
 
< 0.1%
69 3
 
< 0.1%
67 1
 
< 0.1%
65 18
< 0.1%
63 13
< 0.1%

Humidity9am
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)0.1%
Missing2654
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean68.880831
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:49.368553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.029164
Coefficient of variation (CV)0.27626212
Kurtosis-0.037555042
Mean68.880831
Median Absolute Deviation (MAD)13
Skewness-0.48396899
Sum9836596
Variance362.1091
MonotonicityNot monotonic
2022-12-28T16:57:49.478828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 3391
 
2.3%
70 3026
 
2.1%
69 3023
 
2.1%
65 3014
 
2.1%
68 3011
 
2.1%
71 2976
 
2.0%
66 2973
 
2.0%
67 2950
 
2.0%
74 2917
 
2.0%
72 2914
 
2.0%
Other values (91) 112611
77.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5
 
< 0.1%
2 8
 
< 0.1%
3 10
 
< 0.1%
4 20
 
< 0.1%
5 27
 
< 0.1%
6 37
< 0.1%
7 43
< 0.1%
8 56
< 0.1%
9 71
< 0.1%
ValueCountFrequency (%)
100 2863
2.0%
99 3391
2.3%
98 2099
1.4%
97 1789
1.2%
96 1609
1.1%
95 1636
1.1%
94 1764
1.2%
93 1862
1.3%
92 1755
1.2%
91 1869
1.3%

Humidity3pm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)0.1%
Missing4507
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean51.539116
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:49.588665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.795902
Coefficient of variation (CV)0.40349745
Kurtosis-0.51136325
Mean51.539116
Median Absolute Deviation (MAD)14
Skewness0.033614368
Sum7264593
Variance432.46953
MonotonicityNot monotonic
2022-12-28T16:57:49.698527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 2751
 
1.9%
55 2738
 
1.9%
57 2728
 
1.9%
53 2697
 
1.9%
59 2690
 
1.8%
58 2643
 
1.8%
54 2642
 
1.8%
50 2624
 
1.8%
51 2621
 
1.8%
60 2615
 
1.8%
Other values (91) 114204
78.5%
(Missing) 4507
 
3.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 26
 
< 0.1%
2 35
 
< 0.1%
3 63
 
< 0.1%
4 113
 
0.1%
5 157
 
0.1%
6 242
0.2%
7 303
0.2%
8 422
0.3%
9 481
0.3%
ValueCountFrequency (%)
100 400
0.3%
99 434
0.3%
98 603
0.4%
97 403
0.3%
96 462
0.3%
95 465
0.3%
94 559
0.4%
93 607
0.4%
92 648
0.4%
91 617
0.4%

Pressure9am
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct546
Distinct (%)0.4%
Missing15065
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean1017.6499
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:49.808352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.1065303
Coefficient of variation (CV)0.0069832759
Kurtosis0.23156262
Mean1017.6499
Median Absolute Deviation (MAD)4.7
Skewness-0.095523637
Sum1.3269646 × 108
Variance50.502773
MonotonicityNot monotonic
2022-12-28T16:57:49.933605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4 816
 
0.6%
1017.9 789
 
0.5%
1016.3 775
 
0.5%
1018.7 775
 
0.5%
1018 769
 
0.5%
1017.3 769
 
0.5%
1015.9 768
 
0.5%
1017.8 766
 
0.5%
1017.2 759
 
0.5%
1017.7 759
 
0.5%
Other values (536) 122650
84.3%
(Missing) 15065
 
10.4%
ValueCountFrequency (%)
980.5 1
< 0.1%
982 1
< 0.1%
982.2 1
< 0.1%
982.3 1
< 0.1%
982.9 2
< 0.1%
983.7 1
< 0.1%
983.9 1
< 0.1%
984.4 1
< 0.1%
984.6 2
< 0.1%
985 1
< 0.1%
ValueCountFrequency (%)
1041 1
 
< 0.1%
1040.9 1
 
< 0.1%
1040.6 2
< 0.1%
1040.5 1
 
< 0.1%
1040.4 3
< 0.1%
1040.3 3
< 0.1%
1040.2 2
< 0.1%
1040.1 3
< 0.1%
1040 1
 
< 0.1%
1039.9 3
< 0.1%

Pressure3pm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct549
Distinct (%)0.4%
Missing15028
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1015.2559
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:50.043343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.0374138
Coefficient of variation (CV)0.0069316651
Kurtosis0.12917156
Mean1015.2559
Median Absolute Deviation (MAD)4.8
Skewness-0.045621405
Sum1.3242186 × 108
Variance49.525193
MonotonicityNot monotonic
2022-12-28T16:57:50.153187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.3 786
 
0.5%
1015.5 783
 
0.5%
1015.6 776
 
0.5%
1015.7 773
 
0.5%
1013.5 767
 
0.5%
1015.1 766
 
0.5%
1015.8 765
 
0.5%
1015.4 756
 
0.5%
1016 747
 
0.5%
1014.8 745
 
0.5%
Other values (539) 122768
84.4%
(Missing) 15028
 
10.3%
ValueCountFrequency (%)
977.1 1
< 0.1%
978.2 1
< 0.1%
979 1
< 0.1%
980.2 2
< 0.1%
981.2 1
< 0.1%
981.4 1
< 0.1%
981.9 1
< 0.1%
982.2 1
< 0.1%
982.6 1
< 0.1%
982.9 1
< 0.1%
ValueCountFrequency (%)
1039.6 1
 
< 0.1%
1038.9 1
 
< 0.1%
1038.5 1
 
< 0.1%
1038.4 1
 
< 0.1%
1038.2 1
 
< 0.1%
1038 1
 
< 0.1%
1037.9 2
< 0.1%
1037.8 2
< 0.1%
1037.7 3
< 0.1%
1037.6 1
 
< 0.1%

Cloud9am
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing55888
Missing (%)38.4%
Infinite0
Infinite (%)0.0%
Mean4.4474613
Minimum0
Maximum9
Zeros8642
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:50.247647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.8871589
Coefficient of variation (CV)0.6491701
Kurtosis-1.5388305
Mean4.4474613
Median Absolute Deviation (MAD)3
Skewness-0.22908183
Sum398368
Variance8.3356862
MonotonicityNot monotonic
2022-12-28T16:57:50.310523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 19972
 
13.7%
1 15687
 
10.8%
8 14697
 
10.1%
0 8642
 
5.9%
6 8171
 
5.6%
2 6500
 
4.5%
3 5914
 
4.1%
5 5567
 
3.8%
4 4420
 
3.0%
9 2
 
< 0.1%
(Missing) 55888
38.4%
ValueCountFrequency (%)
0 8642
5.9%
1 15687
10.8%
2 6500
 
4.5%
3 5914
 
4.1%
4 4420
 
3.0%
5 5567
 
3.8%
6 8171
5.6%
7 19972
13.7%
8 14697
10.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 14697
10.1%
7 19972
13.7%
6 8171
5.6%
5 5567
 
3.8%
4 4420
 
3.0%
3 5914
 
4.1%
2 6500
 
4.5%
1 15687
10.8%
0 8642
5.9%

Cloud3pm
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing59358
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean4.5099301
Minimum0
Maximum9
Zeros4974
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:50.389084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7203573
Coefficient of variation (CV)0.60319279
Kurtosis-1.4565245
Mean4.5099301
Median Absolute Deviation (MAD)2
Skewness-0.22638435
Sum388314
Variance7.4003439
MonotonicityNot monotonic
2022-12-28T16:57:50.474762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 18229
 
12.5%
1 14976
 
10.3%
8 12660
 
8.7%
6 8978
 
6.2%
2 7226
 
5.0%
3 6921
 
4.8%
5 6815
 
4.7%
4 5322
 
3.7%
0 4974
 
3.4%
9 1
 
< 0.1%
(Missing) 59358
40.8%
ValueCountFrequency (%)
0 4974
 
3.4%
1 14976
10.3%
2 7226
 
5.0%
3 6921
 
4.8%
4 5322
 
3.7%
5 6815
 
4.7%
6 8978
6.2%
7 18229
12.5%
8 12660
8.7%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 12660
8.7%
7 18229
12.5%
6 8978
6.2%
5 6815
 
4.7%
4 5322
 
3.7%
3 6921
 
4.8%
2 7226
 
5.0%
1 14976
10.3%
0 4974
 
3.4%

Temp9am
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct441
Distinct (%)0.3%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean16.990631
Minimum-7.2
Maximum40.2
Zeros36
Zeros (%)< 0.1%
Negative443
Negative (%)0.3%
Memory size1.1 MiB
2022-12-28T16:57:50.561440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.4887531
Coefficient of variation (CV)0.38190182
Kurtosis-0.34052334
Mean16.990631
Median Absolute Deviation (MAD)4.6
Skewness0.088539997
Sum2441434.8
Variance42.103917
MonotonicityNot monotonic
2022-12-28T16:57:50.675303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 912
 
0.6%
13.8 900
 
0.6%
14.8 894
 
0.6%
16 882
 
0.6%
14 876
 
0.6%
15 867
 
0.6%
16.6 867
 
0.6%
16.5 856
 
0.6%
13 848
 
0.6%
15.1 846
 
0.6%
Other values (431) 134945
92.8%
(Missing) 1767
 
1.2%
ValueCountFrequency (%)
-7.2 1
 
< 0.1%
-7 1
 
< 0.1%
-6.2 1
 
< 0.1%
-5.9 1
 
< 0.1%
-5.6 2
 
< 0.1%
-5.5 2
 
< 0.1%
-5.3 2
 
< 0.1%
-5.2 5
< 0.1%
-4.9 1
 
< 0.1%
-4.8 2
 
< 0.1%
ValueCountFrequency (%)
40.2 1
< 0.1%
39.4 1
< 0.1%
39.1 1
< 0.1%
39 1
< 0.1%
38.9 1
< 0.1%
38.6 1
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%

Temp3pm
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct502
Distinct (%)0.4%
Missing3609
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean21.68339
Minimum-5.4
Maximum46.7
Zeros17
Zeros (%)< 0.1%
Negative180
Negative (%)0.1%
Memory size1.1 MiB
2022-12-28T16:57:50.781497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.6
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.9366505
Coefficient of variation (CV)0.31990618
Kurtosis-0.13628147
Mean21.68339
Median Absolute Deviation (MAD)4.8
Skewness0.23796036
Sum3075810.6
Variance48.11712
MonotonicityNot monotonic
2022-12-28T16:57:50.875782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 882
 
0.6%
19 869
 
0.6%
18.5 869
 
0.6%
18.4 868
 
0.6%
17.8 859
 
0.6%
19.4 840
 
0.6%
18 839
 
0.6%
19.2 839
 
0.6%
17 834
 
0.6%
19.3 833
 
0.6%
Other values (492) 133319
91.7%
(Missing) 3609
 
2.5%
ValueCountFrequency (%)
-5.4 1
 
< 0.1%
-5.1 1
 
< 0.1%
-4.4 1
 
< 0.1%
-4.2 1
 
< 0.1%
-4.1 1
 
< 0.1%
-4 1
 
< 0.1%
-3.9 2
< 0.1%
-3.8 1
 
< 0.1%
-3.7 3
< 0.1%
-3.5 3
< 0.1%
ValueCountFrequency (%)
46.7 1
 
< 0.1%
46.2 1
 
< 0.1%
46.1 3
< 0.1%
45.9 1
 
< 0.1%
45.8 2
< 0.1%
45.4 1
 
< 0.1%
45.3 2
< 0.1%
45.2 2
< 0.1%
45 1
 
< 0.1%
44.9 1
 
< 0.1%

WindGustDir1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0935996
Minimum0
Maximum15
Zeros9181
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-12-28T16:57:50.975929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q313
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.6941098
Coefficient of variation (CV)0.57997801
Kurtosis-1.2394623
Mean8.0935996
Median Absolute Deviation (MAD)4
Skewness-0.23364456
Sum1177295
Variance22.034667
MonotonicityNot monotonic
2022-12-28T16:57:51.048489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 20241
13.9%
9 9418
 
6.5%
3 9313
 
6.4%
10 9216
 
6.3%
0 9181
 
6.3%
8 9168
 
6.3%
15 9069
 
6.2%
12 8967
 
6.2%
11 8736
 
6.0%
14 8252
 
5.7%
Other values (6) 43899
30.2%
ValueCountFrequency (%)
0 9181
6.3%
1 8104
5.6%
2 7372
5.1%
3 9313
6.4%
4 7133
4.9%
5 6548
4.5%
6 6620
4.6%
7 8122
5.6%
8 9168
6.3%
9 9418
6.5%
ValueCountFrequency (%)
15 9069
6.2%
14 8252
5.7%
13 20241
13.9%
12 8967
6.2%
11 8736
6.0%
10 9216
6.3%
9 9418
6.5%
8 9168
6.3%
7 8122
5.6%
6 6620
 
4.6%

WindDir9am1
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9438265
Minimum0
Maximum16
Zeros9176
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-12-28T16:57:51.158197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q312
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.9217402
Coefficient of variation (CV)0.61956794
Kurtosis-1.1906646
Mean7.9438265
Median Absolute Deviation (MAD)4
Skewness0.035235034
Sum1155509
Variance24.223527
MonotonicityNot monotonic
2022-12-28T16:57:51.268010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 11758
 
8.1%
16 10566
 
7.3%
9 9287
 
6.4%
0 9176
 
6.3%
10 9112
 
6.3%
7 8749
 
6.0%
8 8659
 
6.0%
13 8459
 
5.8%
12 8423
 
5.8%
5 8129
 
5.6%
Other values (7) 53142
36.5%
ValueCountFrequency (%)
0 9176
6.3%
1 7836
5.4%
2 7630
5.2%
3 11758
8.1%
4 7671
5.3%
5 8129
5.6%
6 7980
5.5%
7 8749
6.0%
8 8659
6.0%
9 9287
6.4%
ValueCountFrequency (%)
16 10566
7.3%
15 7024
4.8%
14 7414
5.1%
13 8459
5.8%
12 8423
5.8%
11 7587
5.2%
10 9112
6.3%
9 9287
6.4%
8 8659
6.0%
7 8749
6.0%

WindDir3pm1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0023168
Minimum0
Maximum16
Zeros8472
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-12-28T16:57:51.378285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q312
95-th percentile15
Maximum16
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7398648
Coefficient of variation (CV)0.59231157
Kurtosis-1.1728331
Mean8.0023168
Median Absolute Deviation (MAD)4
Skewness-0.081807845
Sum1164017
Variance22.466318
MonotonicityNot monotonic
2022-12-28T16:57:51.477026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9 10838
 
7.5%
13 10110
 
7.0%
8 9926
 
6.8%
15 9518
 
6.5%
10 9399
 
6.5%
12 9354
 
6.4%
3 8890
 
6.1%
14 8874
 
6.1%
7 8610
 
5.9%
2 8505
 
5.8%
Other values (7) 51436
35.4%
ValueCountFrequency (%)
0 8472
5.8%
1 7857
5.4%
2 8505
5.8%
3 8890
6.1%
4 8263
5.7%
5 6590
4.5%
6 7870
5.4%
7 8610
5.9%
8 9926
6.8%
9 10838
7.5%
ValueCountFrequency (%)
16 4228
 
2.9%
15 9518
6.5%
14 8874
6.1%
13 10110
7.0%
12 9354
6.4%
11 8156
5.6%
10 9399
6.5%
9 10838
7.5%
8 9926
6.8%
7 8610
5.9%

RainToday1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
110319 
1
31880 
2
 
3261

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 110319
75.8%
1 31880
 
21.9%
2 3261
 
2.2%

Length

2022-12-28T16:57:51.597903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:51.723314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 110319
75.8%
1 31880
 
21.9%
2 3261
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 110319
75.8%
1 31880
 
21.9%
2 3261
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110319
75.8%
1 31880
 
21.9%
2 3261
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 110319
75.8%
1 31880
 
21.9%
2 3261
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 110319
75.8%
1 31880
 
21.9%
2 3261
 
2.2%

RainTomorrow1
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
110316 
1
31877 
2
 
3267

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 110316
75.8%
1 31877
 
21.9%
2 3267
 
2.2%

Length

2022-12-28T16:57:51.817478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:51.927253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 110316
75.8%
1 31877
 
21.9%
2 3267
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 110316
75.8%
1 31877
 
21.9%
2 3267
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110316
75.8%
1 31877
 
21.9%
2 3267
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 110316
75.8%
1 31877
 
21.9%
2 3267
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 110316
75.8%
1 31877
 
21.9%
2 3267
 
2.2%

year
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.7698
Minimum2007
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:52.021436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2009
Q12011
median2013
Q32015
95-th percentile2017
Maximum2017
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5376837
Coefficient of variation (CV)0.0012607919
Kurtosis-1.1806776
Mean2012.7698
Median Absolute Deviation (MAD)2
Skewness-0.049356669
Sum2.9277749 × 108
Variance6.4398388
MonotonicityNot monotonic
2022-12-28T16:57:52.115707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2016 17934
12.3%
2014 17885
12.3%
2015 17885
12.3%
2009 16789
11.5%
2010 16782
11.5%
2013 16415
11.3%
2012 15409
10.6%
2011 15407
10.6%
2017 8623
5.9%
2008 2270
 
1.6%
ValueCountFrequency (%)
2007 61
 
< 0.1%
2008 2270
 
1.6%
2009 16789
11.5%
2010 16782
11.5%
2011 15407
10.6%
2012 15409
10.6%
2013 16415
11.3%
2014 17885
12.3%
2015 17885
12.3%
2016 17934
12.3%
ValueCountFrequency (%)
2017 8623
5.9%
2016 17934
12.3%
2015 17885
12.3%
2014 17885
12.3%
2013 16415
11.3%
2012 15409
10.6%
2011 15407
10.6%
2010 16782
11.5%
2009 16789
11.5%
2008 2270
 
1.6%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.399615
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:52.225849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4272616
Coefficient of variation (CV)0.53554184
Kurtosis-1.1918797
Mean6.399615
Median Absolute Deviation (MAD)3
Skewness0.030342867
Sum930888
Variance11.746122
MonotonicityNot monotonic
2022-12-28T16:57:52.320029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 13361
9.2%
5 13353
9.2%
1 13236
9.1%
6 12684
8.7%
8 12028
8.3%
10 12028
8.3%
7 12025
8.3%
11 11669
8.0%
9 11640
8.0%
4 11550
7.9%
Other values (2) 21886
15.0%
ValueCountFrequency (%)
1 13236
9.1%
2 10793
7.4%
3 13361
9.2%
4 11550
7.9%
5 13353
9.2%
6 12684
8.7%
7 12025
8.3%
8 12028
8.3%
9 11640
8.0%
10 12028
8.3%
ValueCountFrequency (%)
12 11093
7.6%
11 11669
8.0%
10 12028
8.3%
9 11640
8.0%
8 12028
8.3%
7 12025
8.3%
6 12684
8.7%
5 13353
9.2%
4 11550
7.9%
3 13361
9.2%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.712258
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-12-28T16:57:52.429814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7947887
Coefficient of variation (CV)0.55974061
Kurtosis-1.1919965
Mean15.712258
Median Absolute Deviation (MAD)8
Skewness0.0090400822
Sum2285505
Variance77.348308
MonotonicityNot monotonic
2022-12-28T16:57:52.539618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 4786
 
3.3%
13 4786
 
3.3%
23 4786
 
3.3%
22 4786
 
3.3%
21 4786
 
3.3%
20 4786
 
3.3%
19 4786
 
3.3%
18 4786
 
3.3%
17 4786
 
3.3%
2 4786
 
3.3%
Other values (21) 97600
67.1%
ValueCountFrequency (%)
1 4786
3.3%
2 4786
3.3%
3 4786
3.3%
4 4786
3.3%
5 4786
3.3%
6 4786
3.3%
7 4786
3.3%
8 4786
3.3%
9 4786
3.3%
10 4786
3.3%
ValueCountFrequency (%)
31 2807
1.9%
30 4351
3.0%
29 4449
3.1%
28 4735
3.3%
27 4735
3.3%
26 4736
3.3%
25 4784
3.3%
24 4785
3.3%
23 4786
3.3%
22 4786
3.3%

week
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9996838
Minimum0
Maximum6
Zeros20745
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-12-28T16:57:52.633846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9992917
Coefficient of variation (CV)0.66650084
Kurtosis-1.248935
Mean2.9996838
Median Absolute Deviation (MAD)2
Skewness0.00080659759
Sum436334
Variance3.9971675
MonotonicityNot monotonic
2022-12-28T16:57:52.728004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 20824
14.3%
2 20800
14.3%
1 20798
14.3%
4 20780
14.3%
6 20779
14.3%
0 20745
14.3%
5 20734
14.3%
ValueCountFrequency (%)
0 20745
14.3%
1 20798
14.3%
2 20800
14.3%
3 20824
14.3%
4 20780
14.3%
5 20734
14.3%
6 20779
14.3%
ValueCountFrequency (%)
6 20779
14.3%
5 20734
14.3%
4 20780
14.3%
3 20824
14.3%
2 20800
14.3%
1 20798
14.3%
0 20745
14.3%

Albany
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:52.837881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:52.947632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Albury
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:53.026211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:53.151614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

AliceSprings
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:53.245765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:53.355546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

BadgerysCreek
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:53.449775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:53.559511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Ballarat
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:53.638012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:53.747798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Bendigo
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:53.857612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:53.967403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Brisbane
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142267 
1
 
3193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Length

2022-12-28T16:57:54.082748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:54.187513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Cairns
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:54.265623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:54.359805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Canberra
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142024 
1
 
3436

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142024
97.6%
1 3436
 
2.4%

Length

2022-12-28T16:57:54.438411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:54.532522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142024
97.6%
1 3436
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 142024
97.6%
1 3436
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142024
97.6%
1 3436
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142024
97.6%
1 3436
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142024
97.6%
1 3436
 
2.4%

Cobar
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:54.595468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:54.689623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

CoffsHarbour
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:54.784410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:54.893631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Dartmoor
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:54.987853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:55.097661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Darwin
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142267 
1
 
3193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Length

2022-12-28T16:57:55.175804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:55.269963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

GoldCoast
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:55.332365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:55.426492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Hobart
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142267 
1
 
3193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Length

2022-12-28T16:57:55.505054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:55.599267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Katherine
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
143882 
1
 
1578

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Length

2022-12-28T16:57:55.693490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:55.787710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Launceston
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:55.865878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:55.987152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Melbourne
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142267 
1
 
3193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Length

2022-12-28T16:57:56.069767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:56.179603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

MelbourneAirport
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:56.258190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:56.352334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Mildura
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:56.430912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:56.556368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Moree
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:56.634964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:56.713489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

MountGambier
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:56.792042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:56.870177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

MountGinini
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:56.948681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:57.042820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Newcastle
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142421 
1
 
3039

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Length

2022-12-28T16:57:57.105247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:57.199415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Nhil
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
143882 
1
 
1578

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Length

2022-12-28T16:57:57.261930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:57.356127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

NorahHead
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142456 
1
 
3004

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142456
97.9%
1 3004
 
2.1%

Length

2022-12-28T16:57:57.434716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:57.528870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142456
97.9%
1 3004
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142456
97.9%
1 3004
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142456
97.9%
1 3004
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142456
97.9%
1 3004
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142456
97.9%
1 3004
 
2.1%

NorfolkIsland
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:57.591796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:57.690970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Nuriootpa
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:57.780119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:57.891551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

PearceRAAF
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:58.000304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:58.110061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Penrith
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142421 
1
 
3039

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Length

2022-12-28T16:57:58.192232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:58.292415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Perth
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142267 
1
 
3193

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Length

2022-12-28T16:57:58.360939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:58.470796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142267
97.8%
1 3193
 
2.2%

PerthAirport
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:58.549328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:58.643511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Portland
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:58.722086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:58.816714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Richmond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:58.895280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:58.994053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Sale
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:59.067618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:59.161963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

SalmonGums
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142459 
1
 
3001

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142459
97.9%
1 3001
 
2.1%

Length

2022-12-28T16:57:59.240500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:59.334625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142459
97.9%
1 3001
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142459
97.9%
1 3001
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142459
97.9%
1 3001
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142459
97.9%
1 3001
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142459
97.9%
1 3001
 
2.1%

Sydney
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142116 
1
 
3344

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142116
97.7%
1 3344
 
2.3%

Length

2022-12-28T16:57:59.413190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:59.507342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142116
97.7%
1 3344
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 142116
97.7%
1 3344
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142116
97.7%
1 3344
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142116
97.7%
1 3344
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142116
97.7%
1 3344
 
2.3%

SydneyAirport
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:57:59.585486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:59.664087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Townsville
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:57:59.742110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:57:59.831028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Tuggeranong
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142421 
1
 
3039

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Length

2022-12-28T16:57:59.925205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:00.019361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142421
97.9%
1 3039
 
2.1%

Uluru
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
143882 
1
 
1578

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Length

2022-12-28T16:58:00.113576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:00.207749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143882
98.9%
1 1578
 
1.1%

WaggaWagga
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:58:00.285885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:00.380084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Walpole
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142454 
1
 
3006

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142454
97.9%
1 3006
 
2.1%

Length

2022-12-28T16:58:00.458617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:00.537053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142454
97.9%
1 3006
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142454
97.9%
1 3006
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142454
97.9%
1 3006
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142454
97.9%
1 3006
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142454
97.9%
1 3006
 
2.1%

Watsonia
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:58:00.615681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:00.709830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Williamtown
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:58:00.772344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:00.866542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Witchcliffe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:58:00.944566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:01.023104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Wollongong
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142420 
1
 
3040

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Length

2022-12-28T16:58:01.101673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:01.179809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142420
97.9%
1 3040
 
2.1%

Woomera
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
142451 
1
 
3009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Length

2022-12-28T16:58:01.258335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-28T16:58:01.352481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 142451
97.9%
1 3009
 
2.1%

Interactions

2022-12-28T16:57:39.014181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:36.098726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:39.065049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.953197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:45.844857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.637151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.414914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.161279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.795867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.369411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.804915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.393893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.966724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.485847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.862313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.231312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.866935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.193658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:24.127708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.406375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.247548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:33.055804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.880539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:39.155630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:36.211658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:39.190516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:42.062908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:45.954733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.747685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.524225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.271049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.905638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.479239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.899115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.503662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:08.080979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.587059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.991244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.341061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.961176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.310455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:24.237539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.525249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.357404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:33.165618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:36.021520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:39.296695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:36.350169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:39.319541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:42.172210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:46.095716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.857510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.646516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.380832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:57.015460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.589056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:03.024560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.597741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:08.181130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.686225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:13.113516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.450794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:18.824410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.429255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:24.362928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.642140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.467190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:33.291080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:36.162955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:39.437728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:36.475595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:39.457512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:42.325807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:46.221046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:49.014225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.775505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.506255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:57.140851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.714489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:03.133909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.723158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:08.296392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.791431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:13.222761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.576163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:18.949834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.554701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:24.472732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.767633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.592614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:33.415980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:36.303990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:39.563584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:36.617260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:39.582398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:42.439134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:46.346852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:49.155597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.885328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.631226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:57.266693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.839954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:03.259334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.832940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:08.406213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.901269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:13.332601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.701956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:19.075250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.680145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:24.613805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.893067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.734082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:33.538260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:36.461092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:39.689025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:36.758313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:39.724737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:42.580227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:46.472353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:49.280997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:52.010786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.757042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:57.376562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.965760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:03.385193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.943071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:08.531577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.995490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:13.442314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.811716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:19.200743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.789446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:24.724024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:28.018553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.859495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:33.651591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:36.633358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2022-12-28T16:57:22.825580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:26.277037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.101672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:31.941885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:34.718983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:37.920521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:41.414521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:37.950752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:40.948999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:43.788301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:47.649076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:50.457692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.203843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:55.855740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:58.442939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:01.785302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:04.474070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.009850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:09.598359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.046493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:14.400378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:16.925451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.267760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:22.935399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:26.422626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.228617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.114198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:34.828826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.030362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:41.587161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.107484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.074475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:43.913694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:47.758860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:50.567554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.313699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:55.964887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:58.569124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:01.895091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:04.577787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.119736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:09.692515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.140623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:14.493998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.035219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.377465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.045205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:26.543944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.337291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.239998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:34.923014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.136380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:41.728192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.233415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.215410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:44.039572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:47.884223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:50.677358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.439141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.090321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:58.678906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.036074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:04.703176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.229476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:09.817984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.250475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:14.603748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.160671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.502925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.170704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:26.653769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.462768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.349756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.048790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.261721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:41.885266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.343261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.341351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:44.180608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.009586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:50.802799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.565035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.215745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:58.804342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.177461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:04.828568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.354905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:09.927785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.360285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:14.713510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.286163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.628833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.296167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:26.794858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.588205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.475228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.205438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.402855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:42.073043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.500014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.466758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:44.330769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.139042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:50.928298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.690516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.341111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:58.929744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.334091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:04.954044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.464690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.053223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.454504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:14.823327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.412056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.738708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.422035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:26.923797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.729786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.600595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.362633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.527736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:42.245284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.641506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.576525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:45.468098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.260806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.054189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.800328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.456942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.039563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.443840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.063852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.590459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.163081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.548632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:14.917516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.521871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.848543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.531327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.046126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.855100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.725955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.488102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.637562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:42.371074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.766894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.701930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:45.577904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.386243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.179646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:53.910071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.560578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.149394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.570271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.175614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.715872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.257303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.658377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.011748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.631675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:20.973990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.656800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.155394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:29.996174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.836745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.613562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.750351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:42.512081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:38.923953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:41.811677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:45.703359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:48.511698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:51.289495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:54.035465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:56.685998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:56:59.263226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:02.679475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:05.283743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:07.841264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:10.382708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:12.752507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:15.121563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:17.741534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:21.083867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:23.876326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:27.280867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:30.121636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:32.945974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:35.755072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-28T16:57:38.873135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-28T16:58:01.540750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
MinTempMaxTempRainfallEvaporationSunshineWindGustSpeedWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmWindGustDir1WindDir9am1WindDir3pm1yearmonthdayweekLocationRainToday1RainTomorrow1AlbanyAlburyAliceSpringsBadgerysCreekBallaratBendigoBrisbaneCairnsCanberraCobarCoffsHarbourDartmoorDarwinGoldCoastHobartKatherineLauncestonMelbourneMelbourneAirportMilduraMoreeMountGambierMountGininiNewcastleNhilNorahHeadNorfolkIslandNuriootpaPearceRAAFPenrithPerthPerthAirportPortlandRichmondSaleSalmonGumsSydneySydneyAirportTownsvilleTuggeranongUluruWaggaWaggaWalpoleWatsoniaWilliamtownWitchcliffeWollongongWoomera
MinTemp1.0000.7350.0210.4460.1300.1930.1780.181-0.2200.030-0.464-0.4680.0670.0120.8980.708-0.142-0.120-0.1610.042-0.2150.0020.0040.2830.0940.0850.0900.0690.0650.0440.1170.0870.1070.2800.1720.0410.0750.0940.3990.1320.0960.1820.1020.0900.0750.0410.0440.0980.2930.0630.0590.1010.1610.0700.0380.0460.0500.0410.0960.0410.0870.0670.0860.0810.2380.1500.0580.0730.0990.0670.0490.0770.1010.043
MaxTemp0.7351.000-0.2920.5220.2090.0940.0230.064-0.466-0.460-0.354-0.444-0.292-0.2870.8900.983-0.221-0.243-0.2000.058-0.1720.0010.0020.3060.1710.1270.1060.0470.1520.0450.1740.0630.1260.2120.0690.0680.0940.0970.3000.1270.1250.2250.0950.0750.0720.0500.0820.0930.5550.0630.0340.0790.1320.0610.0630.0480.0570.0580.1310.0480.0710.0240.0810.0610.1920.0720.1420.0440.0820.0650.0580.0610.0930.094
Rainfall0.021-0.2921.000-0.218-0.1230.1120.0780.0650.4390.436-0.152-0.0620.3680.317-0.154-0.3010.1360.1520.124-0.0170.0150.003-0.0150.0360.1340.0830.0080.0000.0080.0000.0050.0040.0190.0570.0030.0080.0410.0070.0380.0220.0100.0130.0050.0090.0070.0060.0000.0080.0000.0110.0060.0100.0030.0080.0060.0000.0050.0060.0050.0000.0090.0090.0110.0080.0350.0000.0000.0030.0030.0050.0100.0000.0210.011
Evaporation0.4460.522-0.2181.0000.2380.1790.1510.146-0.402-0.284-0.271-0.294-0.199-0.2040.5180.510-0.099-0.109-0.0800.025-0.023-0.006-0.0190.1110.0260.0220.0110.0170.1290.0160.0170.0160.0160.0000.0140.0420.0160.0130.0160.0170.0140.0000.0110.0040.0040.0180.0640.0130.0170.0170.0110.0160.0110.0000.0160.0170.0120.0120.0130.0140.0160.0160.0150.0130.0150.0170.0110.0120.0160.0040.0580.0160.0170.200
Sunshine0.1300.209-0.1230.2381.0000.0550.1130.121-0.263-0.247-0.023-0.043-0.476-0.5070.1790.229-0.058-0.135-0.048-0.2020.0060.001-0.0170.2830.1500.1870.0940.1370.1510.1370.1370.1370.1420.1090.0310.0880.0250.1000.2050.1370.1480.0980.1370.1300.1260.1330.0740.1010.1370.1370.0980.1360.1000.1210.1560.1370.1610.1560.1000.1370.0410.1360.1270.1200.1310.1370.0980.1070.1360.1260.0250.1370.1370.068
WindGustSpeed0.1930.0940.1120.1790.0551.0000.5690.658-0.230-0.052-0.417-0.3730.0520.0880.1710.0610.100-0.0690.105-0.0220.050-0.011-0.0100.1520.1060.1620.1650.1140.0410.0860.0580.0160.1510.0430.0190.0420.0210.0120.0500.0340.0990.0300.0790.0730.0830.0360.0240.0330.0680.1650.0230.0360.0360.0160.0460.1250.0610.0520.0350.0680.0390.0280.0480.0800.0570.0540.0260.0550.0150.0380.0210.0180.0650.049
WindSpeed9am0.1780.0230.0780.1510.1130.5691.0000.486-0.287-0.036-0.213-0.1630.0170.0470.1360.0120.007-0.1560.030-0.0160.050-0.008-0.0090.1650.0690.0630.0440.0790.0240.0830.0980.0280.1200.0650.0610.0400.0230.0380.0260.0810.0350.0320.0690.1040.1210.0330.0730.0570.0300.1010.0300.0410.1030.0190.0300.1000.0660.0550.0330.0870.0330.0630.0380.0600.0370.0810.0420.0280.0270.0660.0290.0270.0410.093
WindSpeed3pm0.1810.0640.0650.1460.1210.6580.4861.000-0.1580.025-0.281-0.2390.0470.0170.1720.0440.086-0.0130.072-0.0310.056-0.011-0.0060.1610.0580.0730.0440.0690.0450.0840.0660.0290.1420.0650.0170.0650.0510.0220.0760.1260.0250.0430.0600.0630.0760.0530.0280.0650.0560.1710.0290.0480.0670.0300.0420.1380.0870.0540.0410.0660.0660.0280.0390.1210.1190.0630.0290.0400.0410.0600.0600.0430.0650.033
Humidity9am-0.220-0.4660.439-0.402-0.263-0.230-0.287-0.1581.0000.6370.1270.1740.4820.376-0.443-0.4600.0640.1920.0460.012-0.0890.0170.0080.2070.2630.1900.0700.0450.3300.0590.1270.0210.0850.0770.0460.1210.0430.1470.0700.0630.0650.0400.0840.0480.0410.0500.0750.0530.1350.0450.0340.0610.0670.0360.0770.0560.0640.0940.0780.0630.0790.0520.0440.0480.0820.0410.1970.0350.0590.0790.0280.0470.0530.144
Humidity3pm0.030-0.4600.436-0.284-0.247-0.052-0.0360.0250.6371.000-0.0220.0580.5300.540-0.182-0.5090.0590.162-0.005-0.011-0.0140.0140.0020.2300.2700.3480.1230.0380.2810.0480.0690.0420.0770.0930.0680.1440.0940.0780.0230.1010.0640.0430.0560.0510.0400.1270.1370.0430.1880.0410.0390.1330.1330.0540.0730.0630.0640.0790.1150.0570.0580.0880.0590.0300.0920.0670.1950.0710.1200.0400.0290.0730.1290.204
Pressure9am-0.464-0.354-0.152-0.271-0.023-0.417-0.213-0.2810.127-0.0221.0000.960-0.122-0.144-0.438-0.310-0.1230.030-0.1270.0240.045-0.0190.0010.1070.1430.1760.0170.0200.0400.0170.0160.0150.0450.0980.0340.0190.0260.0270.1650.0440.1540.0920.0750.0220.0220.0240.0300.0251.0001.0000.0140.0160.0460.0250.0211.0000.0180.0190.0310.0220.0291.0000.0180.0170.0810.0250.0190.0180.0110.0230.0180.0170.0130.024
Pressure3pm-0.468-0.444-0.062-0.294-0.043-0.373-0.163-0.2390.1740.0580.9601.000-0.051-0.080-0.481-0.408-0.0330.110-0.0320.0190.032-0.0190.0010.1180.0920.1660.0310.0120.0590.0070.0250.0130.0430.1100.0210.0180.0240.0400.1890.0400.1450.1250.0650.0220.0280.0200.0320.0351.0001.0000.0180.0170.0510.0310.0271.0000.0260.0270.0450.0070.0311.0000.0200.0170.0900.0100.0320.0130.0250.0240.0160.0350.0190.018
Cloud9am0.067-0.2920.368-0.199-0.4760.0520.0170.0470.4820.530-0.122-0.0511.0000.615-0.146-0.3060.0790.1150.0610.079-0.0120.0070.0150.1720.2300.2310.0610.1290.1281.0000.1170.0670.0560.0690.0540.0860.0251.0000.0741.0000.0640.1200.0880.0850.1040.0880.0690.0711.0000.0691.0001.0000.0750.0600.0641.0000.0820.0810.1430.0870.0791.0000.0520.0530.0701.0000.0370.0481.0000.1040.0241.0000.1230.158
Cloud3pm0.012-0.2870.317-0.204-0.5070.0880.0470.0170.3760.540-0.144-0.0800.6151.000-0.135-0.3310.0700.0680.0640.056-0.004-0.0000.0060.1650.2010.2840.0440.1050.1251.0000.1720.0540.0610.0430.0640.0500.0241.0000.0541.0000.0670.0890.0870.0920.1080.0560.0450.0701.0000.0511.0001.0000.0570.0700.0581.0000.0910.0910.1360.0720.0651.0000.0450.0440.0561.0000.0370.0491.0000.1080.0351.0000.1260.159
Temp9am0.8980.890-0.1540.5180.1790.1710.1360.172-0.443-0.182-0.438-0.481-0.146-0.1351.0000.865-0.183-0.199-0.1840.045-0.151-0.0010.0010.2930.0890.0420.0860.0810.1600.0510.1410.0790.1230.2510.1190.0370.1060.1070.3300.1380.1120.2060.1190.0670.0840.0420.0620.0910.4130.0600.0630.0850.1420.0670.0520.0470.0510.0510.1110.0450.0820.0460.0620.0700.2500.1140.1080.0640.0830.0770.0640.0730.0890.047
Temp3pm0.7080.983-0.3010.5100.2290.0610.0120.044-0.460-0.509-0.310-0.408-0.306-0.3310.8651.000-0.233-0.244-0.2120.051-0.1850.0000.0020.3060.1700.1410.0940.0410.1650.0410.1770.0590.1270.2050.0700.0720.0960.0940.3070.1230.1360.1770.0930.0620.0720.0510.0900.0940.5630.0450.0300.0900.1300.0550.0640.0490.0570.0580.1310.0480.0710.0240.0830.0630.1920.0690.1530.0380.0860.0670.0560.0630.0980.095
WindGustDir1-0.142-0.2210.136-0.099-0.0580.1000.0070.0860.0640.059-0.123-0.0330.0790.070-0.183-0.2331.0000.3570.567-0.0250.045-0.0020.0030.2700.1010.0910.3630.0490.1020.0480.0850.0780.1140.1530.1030.0550.0760.0770.0980.1190.1310.0810.1760.1180.1210.0530.0830.0540.0840.3630.0310.0850.0660.0640.0860.0330.1270.0960.0500.0480.0910.0390.1350.0730.1950.0630.0780.0900.0590.0960.0710.0970.0930.071
WindDir9am1-0.120-0.2430.152-0.109-0.135-0.069-0.156-0.0130.1920.1620.0300.1100.1150.068-0.199-0.2440.3571.0000.299-0.0140.003-0.0030.0090.2220.1280.0760.0280.0700.0820.1170.0750.0610.1100.1820.0550.0630.1360.0610.0870.1130.1470.0650.0700.0980.1040.0430.1150.0500.0870.1860.0250.0570.0580.0780.0790.0710.0890.1110.0410.0950.1490.0420.2010.0990.1010.0730.0930.1630.0360.0560.1110.0500.0630.062
WindDir3pm1-0.161-0.2000.124-0.080-0.0480.1050.0300.0720.046-0.005-0.127-0.0320.0610.064-0.184-0.2120.5670.2991.0000.0070.034-0.0020.0060.2140.1020.0660.1340.0550.0910.0480.0650.0390.1290.1450.0860.0420.0860.0540.0950.0940.0910.0710.1430.0920.0880.0440.0500.0600.1210.2270.0420.0900.0600.0620.0620.0320.1050.0860.0550.0560.0820.0290.1020.0840.2320.0820.0520.0630.0670.0880.0590.0890.0860.051
year0.0420.058-0.0170.025-0.202-0.022-0.016-0.0310.012-0.0110.0240.0190.0790.0560.0450.051-0.025-0.0140.0071.000-0.109-0.0050.0020.0920.0410.0410.0020.0020.0020.0170.0020.0020.0490.0020.1340.0170.0170.0170.0490.0020.0490.0960.0020.0490.0170.0170.0170.0020.0020.0020.0960.0170.0170.0170.0170.0020.0490.0170.0170.0170.0170.0170.1030.0170.0020.0020.0960.0170.0170.0170.0170.0170.0020.017
month-0.215-0.1720.015-0.0230.0060.0500.0500.056-0.089-0.0140.0450.032-0.012-0.004-0.151-0.1850.0450.0030.034-0.1091.0000.009-0.0040.0000.0410.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.000
day0.0020.0010.003-0.0060.001-0.011-0.008-0.0110.0170.014-0.019-0.0190.007-0.000-0.0010.000-0.002-0.003-0.002-0.0050.0091.0000.0040.0000.0090.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
week0.0040.002-0.015-0.019-0.017-0.010-0.009-0.0060.0080.0020.0010.0010.0150.0060.0010.0020.0030.0090.0060.002-0.0040.0041.0000.0000.0420.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Location0.2830.3060.0360.1110.2830.1520.1650.1610.2070.2300.1070.1180.1720.1650.2930.3060.2700.2220.2140.0920.0000.0000.0001.0000.2250.2251.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
RainToday10.0940.1710.1340.0260.1500.1060.0690.0580.2630.2700.1430.0920.2300.2010.0890.1700.1010.1280.1020.0410.0410.0090.0420.2251.0000.4520.0300.0140.0540.0090.0210.0240.0120.0330.0230.0360.0240.0300.0270.0120.0210.0150.0180.2180.0220.0460.0400.0340.0280.0090.0210.0180.0300.0220.0600.0080.0240.0250.0520.0120.0190.0230.0250.0240.0270.0150.0370.0190.0540.0200.1280.0250.0050.057
RainTomorrow10.0850.1270.0830.0220.1870.1620.0630.0730.1900.3480.1760.1660.2310.2840.0420.1410.0910.0760.0660.0410.0400.0120.0420.2250.4521.0000.0300.0140.0540.0090.0210.0240.0120.0330.0230.0360.0240.0300.0270.0120.0210.0150.0180.2180.0220.0460.0400.0340.0280.0090.0210.0180.0300.0220.0600.0080.0240.0250.0530.0120.0190.0230.0240.0240.0270.0150.0370.0190.0540.0200.1270.0250.0050.057
Albany0.0900.1060.0080.0110.0940.1650.0440.0440.0700.1230.0170.0310.0610.0440.0860.0940.3630.0280.1340.0020.0000.0000.0001.0000.0300.0301.0000.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Albury0.0690.0470.0000.0170.1370.1140.0790.0690.0450.0380.0200.0120.1290.1050.0810.0410.0490.0700.0550.0020.0000.0000.0001.0000.0140.0140.0211.0000.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
AliceSprings0.0650.1520.0080.1290.1510.0410.0240.0450.3300.2810.0400.0590.1280.1250.1600.1650.1020.0820.0910.0020.0000.0000.0001.0000.0540.0540.0210.0211.0000.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
BadgerysCreek0.0440.0450.0000.0160.1370.0860.0830.0840.0590.0480.0170.0071.0001.0000.0510.0410.0480.1170.0480.0170.0000.0000.0001.0000.0090.0090.0210.0210.0211.0000.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Ballarat0.1170.1740.0050.0170.1370.0580.0980.0660.1270.0690.0160.0250.1170.1720.1410.1770.0850.0750.0650.0020.0000.0000.0001.0000.0210.0210.0210.0210.0210.0211.0000.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Bendigo0.0870.0630.0040.0160.1370.0160.0280.0290.0210.0420.0150.0130.0670.0540.0790.0590.0780.0610.0390.0020.0000.0000.0001.0000.0240.0240.0210.0210.0210.0210.0211.0000.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Brisbane0.1070.1260.0190.0160.1420.1510.1200.1420.0850.0770.0450.0430.0560.0610.1230.1270.1140.1100.1290.0490.0000.0000.0001.0000.0120.0120.0220.0220.0220.0210.0220.0221.0000.0220.0230.0210.0210.0210.0220.0220.0220.0150.0220.0220.0210.0210.0210.0220.0220.0220.0150.0210.0210.0210.0210.0220.0220.0210.0210.0210.0210.0210.0230.0210.0220.0220.0150.0210.0210.0210.0210.0210.0220.021
Cairns0.2800.2120.0570.0000.1090.0430.0650.0650.0770.0930.0980.1100.0690.0430.2510.2050.1530.1820.1450.0020.0000.0000.0001.0000.0330.0330.0210.0210.0210.0210.0210.0210.0221.0000.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Canberra0.1720.0690.0030.0140.0310.0190.0610.0170.0460.0680.0340.0210.0540.0640.1190.0700.1030.0550.0860.1340.0000.0000.0001.0000.0230.0230.0220.0220.0220.0220.0220.0220.0230.0221.0000.0220.0220.0220.0230.0220.0230.0160.0220.0230.0220.0220.0220.0220.0220.0220.0160.0220.0220.0220.0220.0220.0230.0220.0220.0220.0220.0220.0240.0220.0220.0220.0160.0220.0220.0220.0220.0220.0220.022
Cobar0.0410.0680.0080.0420.0880.0420.0400.0650.1210.1440.0190.0180.0860.0500.0370.0720.0550.0630.0420.0170.0000.0000.0001.0000.0360.0360.0210.0210.0210.0210.0210.0210.0210.0210.0221.0000.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
CoffsHarbour0.0750.0940.0410.0160.0250.0210.0230.0510.0430.0940.0260.0240.0250.0240.1060.0960.0760.1360.0860.0170.0000.0000.0001.0000.0240.0240.0210.0210.0210.0210.0210.0210.0210.0210.0220.0211.0000.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Dartmoor0.0940.0970.0070.0130.1000.0120.0380.0220.1470.0780.0270.0401.0001.0000.1070.0940.0770.0610.0540.0170.0000.0000.0001.0000.0300.0300.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0211.0000.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Darwin0.3990.3000.0380.0160.2050.0500.0260.0760.0700.0230.1650.1890.0740.0540.3300.3070.0980.0870.0950.0490.0000.0000.0001.0000.0270.0270.0220.0220.0220.0210.0220.0220.0220.0220.0230.0210.0210.0211.0000.0220.0220.0150.0220.0220.0210.0210.0210.0220.0220.0220.0150.0210.0210.0210.0210.0220.0220.0210.0210.0210.0210.0210.0230.0210.0220.0220.0150.0210.0210.0210.0210.0210.0220.021
GoldCoast0.1320.1270.0220.0170.1370.0340.0810.1260.0630.1010.0440.0401.0001.0000.1380.1230.1190.1130.0940.0020.0000.0000.0001.0000.0120.0120.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0221.0000.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Hobart0.0960.1250.0100.0140.1480.0990.0350.0250.0650.0640.1540.1450.0640.0670.1120.1360.1310.1470.0910.0490.0000.0000.0001.0000.0210.0210.0220.0220.0220.0210.0220.0220.0220.0220.0230.0210.0210.0210.0220.0221.0000.0150.0220.0220.0210.0210.0210.0220.0220.0220.0150.0210.0210.0210.0210.0220.0220.0210.0210.0210.0210.0210.0230.0210.0220.0220.0150.0210.0210.0210.0210.0210.0220.021
Katherine0.1820.2250.0130.0000.0980.0300.0320.0430.0400.0430.0920.1250.1200.0890.2060.1770.0810.0650.0710.0960.0030.0000.0001.0000.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0160.0150.0150.0150.0150.0150.0151.0000.0150.0150.0150.0150.0150.0150.0150.0150.0100.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0160.0150.0150.0150.0100.0150.0150.0150.0150.0150.0150.015
Launceston0.1020.0950.0050.0110.1370.0790.0690.0600.0840.0560.0750.0650.0880.0870.1190.0930.1760.0700.1430.0020.0000.0000.0001.0000.0180.0180.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0151.0000.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Melbourne0.0900.0750.0090.0040.1300.0730.1040.0630.0480.0510.0220.0220.0850.0920.0670.0620.1180.0980.0920.0490.0000.0000.0001.0000.2180.2180.0220.0220.0220.0210.0220.0220.0220.0220.0230.0210.0210.0210.0220.0220.0220.0150.0221.0000.0210.0210.0210.0220.0220.0220.0150.0210.0210.0210.0210.0220.0220.0210.0210.0210.0210.0210.0230.0210.0220.0220.0150.0210.0210.0210.0210.0210.0220.021
MelbourneAirport0.0750.0720.0070.0040.1260.0830.1210.0760.0410.0400.0220.0280.1040.1080.0840.0720.1210.1040.0880.0170.0000.0000.0001.0000.0220.0220.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0211.0000.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Mildura0.0410.0500.0060.0180.1330.0360.0330.0530.0500.1270.0240.0200.0880.0560.0420.0510.0530.0430.0440.0170.0000.0000.0001.0000.0460.0460.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0211.0000.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Moree0.0440.0820.0000.0640.0740.0240.0730.0280.0750.1370.0300.0320.0690.0450.0620.0900.0830.1150.0500.0170.0000.0000.0001.0000.0400.0400.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0211.0000.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
MountGambier0.0980.0930.0080.0130.1010.0330.0570.0650.0530.0430.0250.0350.0710.0700.0910.0940.0540.0500.0600.0020.0000.0000.0001.0000.0340.0340.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0211.0000.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
MountGinini0.2930.5550.0000.0170.1370.0680.0300.0560.1350.1881.0001.0001.0001.0000.4130.5630.0840.0870.1210.0020.0000.0000.0001.0000.0280.0280.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0211.0000.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Newcastle0.0630.0630.0110.0170.1370.1650.1010.1710.0450.0411.0001.0000.0690.0510.0600.0450.3630.1860.2270.0020.0000.0000.0001.0000.0090.0090.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0211.0000.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Nhil0.0590.0340.0060.0110.0980.0230.0300.0290.0340.0390.0140.0181.0001.0000.0630.0300.0310.0250.0420.0960.0030.0000.0001.0000.0210.0210.0150.0150.0150.0150.0150.0150.0150.0150.0160.0150.0150.0150.0150.0150.0150.0100.0150.0150.0150.0150.0150.0150.0150.0151.0000.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0160.0150.0150.0150.0100.0150.0150.0150.0150.0150.0150.015
NorahHead0.1010.0790.0100.0160.1360.0360.0410.0480.0610.1330.0160.0171.0001.0000.0850.0900.0850.0570.0900.0170.0000.0000.0001.0000.0180.0180.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0151.0000.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
NorfolkIsland0.1610.1320.0030.0110.1000.0360.1030.0670.0670.1330.0460.0510.0750.0570.1420.1300.0660.0580.0600.0170.0000.0000.0001.0000.0300.0300.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0211.0000.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Nuriootpa0.0700.0610.0080.0000.1210.0160.0190.0300.0360.0540.0250.0310.0600.0700.0670.0550.0640.0780.0620.0170.0000.0000.0001.0000.0220.0220.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0211.0000.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
PearceRAAF0.0380.0630.0060.0160.1560.0460.0300.0420.0770.0730.0210.0270.0640.0580.0520.0640.0860.0790.0620.0170.0000.0000.0001.0000.0600.0600.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0211.0000.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Penrith0.0460.0480.0000.0170.1370.1250.1000.1380.0560.0631.0001.0001.0001.0000.0470.0490.0330.0710.0320.0020.0000.0000.0001.0000.0080.0080.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0211.0000.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Perth0.0500.0570.0050.0120.1610.0610.0660.0870.0640.0640.0180.0260.0820.0910.0510.0570.1270.0890.1050.0490.0000.0000.0001.0000.0240.0240.0220.0220.0220.0210.0220.0220.0220.0220.0230.0210.0210.0210.0220.0220.0220.0150.0220.0220.0210.0210.0210.0220.0220.0220.0150.0210.0210.0210.0210.0221.0000.0210.0210.0210.0210.0210.0230.0210.0220.0220.0150.0210.0210.0210.0210.0210.0220.021
PerthAirport0.0410.0580.0060.0120.1560.0520.0550.0540.0940.0790.0190.0270.0810.0910.0510.0580.0960.1110.0860.0170.0000.0000.0001.0000.0250.0250.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0211.0000.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Portland0.0960.1310.0050.0130.1000.0350.0330.0410.0780.1150.0310.0450.1430.1360.1110.1310.0500.0410.0550.0170.0000.0000.0001.0000.0520.0530.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0211.0000.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Richmond0.0410.0480.0000.0140.1370.0680.0870.0660.0630.0570.0220.0070.0870.0720.0450.0480.0480.0950.0560.0170.0000.0000.0001.0000.0120.0120.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0211.0000.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Sale0.0870.0710.0090.0160.0410.0390.0330.0660.0790.0580.0290.0310.0790.0650.0820.0710.0910.1490.0820.0170.0000.0000.0001.0000.0190.0190.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0211.0000.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
SalmonGums0.0670.0240.0090.0160.1360.0280.0630.0280.0520.0881.0001.0001.0001.0000.0460.0240.0390.0420.0290.0170.0000.0000.0001.0000.0230.0230.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0211.0000.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Sydney0.0860.0810.0110.0150.1270.0480.0380.0390.0440.0590.0180.0200.0520.0450.0620.0830.1350.2010.1020.1030.0000.0000.0001.0000.0250.0240.0220.0220.0220.0220.0220.0220.0230.0220.0240.0220.0220.0220.0230.0220.0230.0160.0220.0230.0220.0220.0220.0220.0220.0220.0160.0220.0220.0220.0220.0220.0230.0220.0220.0220.0220.0221.0000.0220.0220.0220.0160.0220.0220.0220.0220.0220.0220.022
SydneyAirport0.0810.0610.0080.0130.1200.0800.0600.1210.0480.0300.0170.0170.0530.0440.0700.0630.0730.0990.0840.0170.0000.0000.0001.0000.0240.0240.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0221.0000.0210.0210.0150.0210.0210.0210.0210.0210.0210.021
Townsville0.2380.1920.0350.0150.1310.0570.0370.1190.0820.0920.0810.0900.0700.0560.2500.1920.1950.1010.2320.0020.0000.0000.0001.0000.0270.0270.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0211.0000.0210.0150.0210.0210.0210.0210.0210.0210.021
Tuggeranong0.1500.0720.0000.0170.1370.0540.0810.0630.0410.0670.0250.0101.0001.0000.1140.0690.0630.0730.0820.0020.0000.0000.0001.0000.0150.0150.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0211.0000.0150.0210.0210.0210.0210.0210.0210.021
Uluru0.0580.1420.0000.0110.0980.0260.0420.0290.1970.1950.0190.0320.0370.0370.1080.1530.0780.0930.0520.0960.0030.0000.0001.0000.0370.0370.0150.0150.0150.0150.0150.0150.0150.0150.0160.0150.0150.0150.0150.0150.0150.0100.0150.0150.0150.0150.0150.0150.0150.0150.0100.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0150.0160.0150.0150.0151.0000.0150.0150.0150.0150.0150.0150.015
WaggaWagga0.0730.0440.0030.0120.1070.0550.0280.0400.0350.0710.0180.0130.0480.0490.0640.0380.0900.1630.0630.0170.0000.0000.0001.0000.0190.0190.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0151.0000.0210.0210.0210.0210.0210.021
Walpole0.0990.0820.0030.0160.1360.0150.0270.0410.0590.1200.0110.0251.0001.0000.0830.0860.0590.0360.0670.0170.0000.0000.0001.0000.0540.0540.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0211.0000.0210.0210.0210.0210.021
Watsonia0.0670.0650.0050.0040.1260.0380.0660.0600.0790.0400.0230.0240.1040.1080.0770.0670.0960.0560.0880.0170.0000.0000.0001.0000.0200.0200.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0211.0000.0210.0210.0210.021
Williamtown0.0490.0580.0100.0580.0250.0210.0290.0600.0280.0290.0180.0160.0240.0350.0640.0560.0710.1110.0590.0170.0000.0000.0001.0000.1280.1270.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0211.0000.0210.0210.021
Witchcliffe0.0770.0610.0000.0160.1370.0180.0270.0430.0470.0730.0170.0351.0001.0000.0730.0630.0970.0500.0890.0170.0000.0000.0001.0000.0250.0250.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0211.0000.0210.021
Wollongong0.1010.0930.0210.0170.1370.0650.0410.0650.0530.1290.0130.0190.1230.1260.0890.0980.0930.0630.0860.0020.0000.0000.0001.0000.0050.0050.0210.0210.0210.0210.0210.0210.0220.0210.0220.0210.0210.0210.0220.0210.0220.0150.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0211.0000.021
Woomera0.0430.0940.0110.2000.0680.0490.0930.0330.1440.2040.0240.0180.1580.1590.0470.0950.0710.0620.0510.0170.0000.0000.0001.0000.0570.0570.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0150.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0210.0220.0210.0210.0210.0150.0210.0210.0210.0210.0210.0211.000

Missing values

2022-12-28T16:57:44.261804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-28T16:57:45.163847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-28T16:57:46.591639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustSpeedWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmWindGustDir1WindDir9am1WindDir3pm1RainToday1RainTomorrow1yearmonthdayweekAlbanyAlburyAliceSpringsBadgerysCreekBallaratBendigoBrisbaneCairnsCanberraCobarCoffsHarbourDartmoorDarwinGoldCoastHobartKatherineLauncestonMelbourneMelbourneAirportMilduraMoreeMountGambierMountGininiNewcastleNhilNorahHeadNorfolkIslandNuriootpaPearceRAAFPenrithPerthPerthAirportPortlandRichmondSaleSalmonGumsSydneySydneyAirportTownsvilleTuggeranongUluruWaggaWaggaWalpoleWatsoniaWilliamtownWitchcliffeWollongongWoomera
0Albury13.422.90.64.00.044.020.024.071.022.01007.71007.18.0NaN16.921.81313140020081210010000000000000000000000000000000000000000000000
1Albury7.425.10.04.00.044.04.022.044.025.01010.61007.8NaNNaN17.224.3146150020081221010000000000000000000000000000000000000000000000
2Albury12.925.70.04.00.046.019.026.038.030.01007.61008.7NaN2.021.023.21513150020081232010000000000000000000000000000000000000000000000
3Albury9.228.00.04.00.024.011.09.045.016.01017.61012.8NaNNaN18.126.54900020081243010000000000000000000000000000000000000000000000
4Albury17.532.31.04.00.041.07.020.082.033.01010.81006.07.08.017.829.713170020081254010000000000000000000000000000000000000000000000
5Albury14.629.70.24.00.056.019.024.055.023.01009.21005.4NaNNaN20.628.91413130020081265010000000000000000000000000000000000000000000000
6Albury14.325.00.04.00.050.020.024.049.019.01009.61008.21.0NaN18.124.61312130020081276010000000000000000000000000000000000000000000000
7Albury7.726.70.04.00.035.06.017.048.019.01013.41010.1NaNNaN16.325.51310130020081280010000000000000000000000000000000000000000000000
8Albury9.731.90.04.00.080.07.028.042.09.01008.91003.6NaNNaN18.330.26970120081291010000000000000000000000000000000000000000000000
9Albury13.130.11.44.00.028.015.011.058.027.01007.01005.7NaNNaN20.128.21381010200812102010000000000000000000000000000000000000000000000
LocationMinTempMaxTempRainfallEvaporationSunshineWindGustSpeedWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmWindGustDir1WindDir9am1WindDir3pm1RainToday1RainTomorrow1yearmonthdayweekAlbanyAlburyAliceSpringsBadgerysCreekBallaratBendigoBrisbaneCairnsCanberraCobarCoffsHarbourDartmoorDarwinGoldCoastHobartKatherineLauncestonMelbourneMelbourneAirportMilduraMoreeMountGambierMountGininiNewcastleNhilNorahHeadNorfolkIslandNuriootpaPearceRAAFPenrithPerthPerthAirportPortlandRichmondSaleSalmonGumsSydneySydneyAirportTownsvilleTuggeranongUluruWaggaWaggaWalpoleWatsoniaWilliamtownWitchcliffeWollongongWoomera
145450Uluru5.224.30.04.00.024.011.011.053.024.01023.81020.0NaNNaN12.323.30900020176164000000000000000000000000000000000000000010000000
145451Uluru6.423.40.04.00.031.015.017.053.025.01025.81023.0NaNNaN11.223.12820020176175000000000000000000000000000000000000000010000000
145452Uluru8.020.70.04.00.041.019.026.056.032.01028.11024.3NaN7.011.620.02900020176186000000000000000000000000000000000000000010000000
145453Uluru7.420.60.04.00.035.015.017.063.033.01027.21023.3NaNNaN11.020.30200020176190000000000000000000000000000000000000000010000000
145454Uluru3.521.80.04.00.031.015.013.059.027.01024.71021.2NaNNaN9.420.90200020176201000000000000000000000000000000000000000010000000
145455Uluru2.823.40.04.00.031.013.011.051.024.01024.61020.3NaNNaN10.122.40910020176212000000000000000000000000000000000000000010000000
145456Uluru3.625.30.04.00.022.013.09.056.021.01023.51019.1NaNNaN10.924.56930020176223000000000000000000000000000000000000000010000000
145457Uluru5.426.90.04.00.037.09.09.053.024.01021.01016.8NaNNaN12.526.139140020176234000000000000000000000000000000000000000010000000
145458Uluru7.827.00.04.00.028.013.07.051.024.01019.41016.53.02.015.126.091030020176245000000000000000000000000000000000000000010000000
145459Uluru14.920.00.04.00.035.017.017.062.036.01020.21017.98.08.015.020.913220220176256000000000000000000000000000000000000000010000000